ONLINE COURSE – Tidyverse for Ecologists (TIDY01) https://www.prstats.org/course/tidyverse-for-ecologists-tidy01/
16th - 20th June Please feel free to share! COURSE OVERVIEW - This course comprehensively introduces the Tidyverse and focuses on its use in data science projects. It is designed to give participants a strong foundation in R programming, core Tidyverse packages, and the Tidymodels framework. The course emphasises hands-on projects to apply learned concepts to real-world data analysis and modelling tasks applied to biology. By the end of the course, participants should: Understand the fundamentals of R programming for data analysis. Be proficient in using core Tidyverse packages to clean, transform, and visualise data. Gain an introduction to basic machine learning concepts through the Tidymodels framework. Learn to preprocess, build, evaluate, and interpret models using Tidymodels. Apply Tidyverse and Tidymodels tools to solve real-world problems through hands-on projects. Please email oliverhoo...@prstatistics.com with any questions Day 1: A Short Course in R Basics (9:30 - 17:30) This day provides participants with the foundational R skills required for working with Tidyverse and Tidymodels. It is designed for beginners or those needing a refresher in R programming. Section 1 (R Essentials): This section focuses on R syntax, variables, data types, conditionals (`if`, `else`, `elif`), loops (`for`, `while`), and writing reusable code using functions. Section 2 (Data Structures and File Handling in R): This section emphasises understanding data structures (e.g., vectors, data frames, lists) and handling files by reading/writing data (e.g., CSVs) for manipulation and analysis. Day 2: Fundamentals of Tidyverse I (9:30 - 17:30) This day introduces participants to the foundational concepts of Tidyverse packages and their applications to data science projects. Section 3 (Data Manipulation I): This section covers the basics of data manipulation using `dplyr` functions such as `filter()`, `select()`, `mutate()`, `arrange()`, and `summarise ()`. Participants will learn how to clean, transform, and prepare datasets for analysis. Section 4 (Data Visualisation I): This section introduces the principles of data visualisation using `ggplot2`. Participants will learn how to create basic plots such as scatterplots, bar charts, and line graphs while exploring the grammar of graphics. Day 3: Fundamentals of Tidyverse II (9:30 - 17:30) This day builds on the foundations established in Day 2 and dives deeper into advanced data manipulation and visualisation techniques. Section 5 (Data Manipulation II): This section extends the use of `dplyr` by introducing more complex operations such as joins, grouping with `group_by()`, and working with pipelines using `%>%`. Finally, additional packages will be presented to enhance data manipulation programming. Section 6 (Data Visualisation II): Participants will explore advanced visualisation techniques using extensions of `ggplot2`, such as creating animated plots with the `gganimate` package and interactive visualisations with additional tools. Day 4: Applying Tidyverse Fundamentals to Data Modelling (9:30 - 17:30) This day introduces participants to machine learning concepts using core libraries for statistical modelling and deep learning. Section 7 (Introduction to regression): This section focuses on regression modelling using Tidymodels. Participants will learn to implement linear regression models, evaluate model performance, and interpret results. Section 8 (Introduction to Classification): This section introduces techniques such as support vector machines and neural networks using Tidymodels. Participants will also explore methods for assessing the performance of classification models. Day 5: Data Science Workflow with Tidyverse (9:30 - 17:30) On the final day, participants will apply all their newly acquired skills to solve real-world problems inspired by ecological datasets. Section 9 (The data science workflow): The workflow will be illustrated based on the core packages introduced. The book "R for Data Science" will serve as a base literature for this day Section 10 (Hands-on project): Participants will work through a complete data science workflow, including data cleaning, transformation, visualisation, modelling, and communication of results. -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
